Dina Machuve
Impact in
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- Online Learning and Analytics
- Animal Science and Zoology top 10%
- Livestock and Poultry Management
Papers in
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- ICT in Developing Communities 5
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- Data Stream Mining Techniques 3
- Co-authors
- Neema Mduma (6 shared papers)Khamisi Kalegele (4 shared papers)Sawahiko Shimada (1 shared paper)Baraka Maiseli (1 shared paper)Karen Bradshaw (1 shared paper)Thomas Clemen (1 shared paper)Anael Sam (3 shared papers)Michael Kisangiri (2 shared papers)
- Journals
- Data Science Journal (1 paper)Applied Artificial Intelligence (1 paper)Scientific African (1 paper)Frontiers in Artificial Intelligence (1 paper)SHILAP Revista de lepidopterología (7 papers)
In The Last Decade
Dina Machuve
30 papers receiving 357 citations
Peers
Comparison fields: 5 of 83
- Computer Science Applications 75
- Animal Science and Zoology 60
- Health Information Management 22
- Analytical Chemistry 44
- Health Informatics 6
Countries citing papers authored by Dina Machuve
This map shows the geographic impact of Dina Machuve's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dina Machuve with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dina Machuve more than expected).
Fields of papers citing papers by Dina Machuve
This network shows the impact of papers produced by Dina Machuve. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dina Machuve. The network helps show where Dina Machuve may publish in the future.
Co-authors
The 16 scholars most cited alongside Dina Machuve, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 67 | |
| 2 | 2020 | 64 | |
| 3 | 2020 | 48 | |
| 4 | 2022 | 44 | |
| 5 | 2021 | 36 | |
| 6 | 2019 | 25 | |
| 7 | 2021 | 22 | |
| 8 | 2021 | 18 | |
| 9 | 2021 | 13 | |
| 10 | Overview Applications of Data Mining In Health Care: The Case Study of Arusha Region | 2013 | 10 |
| 11 | 2019 | 7 | |
| 12 | 2019 | 4 | |
| 13 | 2021 | 4 | |
| 14 | 2024 | 3 | |
| 15 | 2018 | 3 | |
| 16 | 2013 | 2 | |
| 17 | 2020 | 2 | |
| 18 | 2021 | 2 | |
| 19 | 2021 | 2 | |
| 20 | 2020 | 2 |
About Dina Machuve
Dina Machuve is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Computer Science Applications and Plant Science, having authored 38 papers that have together received 389 indexed citations. Recurring topics across this work include Smart Agriculture and AI (6 papers), ICT in Developing Communities (5 papers), Online Learning and Analytics (4 papers), Data Stream Mining Techniques (3 papers), Leaf Properties and Growth Measurement (3 papers), Mobile Crowdsensing and Crowdsourcing (2 papers), IoT and Edge/Fog Computing (2 papers) and Mobile Health and mHealth Applications (2 papers). The work is most often cited by research in Computer Science Applications (75 citations), Animal Science and Zoology (60 citations), Health Information Management (22 citations), Analytical Chemistry (44 citations) and Health Informatics (6 citations). Dina Machuve has collaborated with scholars based in Tanzania, Finland and Japan. Frequent co-authors include Neema Mduma, Khamisi Kalegele, Sawahiko Shimada, Baraka Maiseli, Karen Bradshaw, Thomas Clemen, Anael Sam, Michael Kisangiri, Mussa Ally Dida and Pirkko Nykänen. Their work appears in journals such as Data Science Journal, Applied Artificial Intelligence, Scientific African, Frontiers in Artificial Intelligence and SHILAP Revista de lepidopterología.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.